Optimal Charging Strategy for Electric Vehicles Based on Hybrid Hierarchical Framework

Kai Deng, Yun Zhang, Zhongwei Chen, Haotian Liu, Peizhe Li, Zheng Lan
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Abstract

The massive roll-out of electric vehicles burden the electricity grid with stochastic load. It is imperative to address this issue either by expanding and reinvesting the network or regulating the charging load in a smart way. This paper proposed a novel hybrid hierarchical strategy for electric vehicle (EV) charging. By synthesizing the strength of the two mainstream control strategies-centralized and decentralized control strategy, it divides the charging process into two levels with different optimal objectives. Considering the stochastic of the charging behavior, it deploys MATSim to simulate the driving patterns and aggregate the EV with similar driving patterns into a so-called Virtual Battery Aggregation (VBA) Model. To verify the effectiveness of the proposed method, an 18-bus system is simulated and 4 different control methods are compared and analyzed regarding to peak hour node voltage, line load, and daily load curve.
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基于混合动力分层框架的电动汽车最优充电策略
电动汽车的大规模推广给电网带来了随机负荷。解决这一问题势在必行,要么通过扩大和再投资电网,要么以一种智能的方式调节充电负荷。针对电动汽车充电问题,提出了一种新的混合分层充电策略。通过综合两种主流控制策略——集中式控制策略和分散式控制策略的强度,将充电过程划分为具有不同最优目标的两个层次。考虑到充电行为的随机性,利用MATSim软件对驾驶模式进行模拟,并将具有相似驾驶模式的电动汽车聚合到一个虚拟电池聚合(VBA)模型中。为了验证该方法的有效性,对一个18母线系统进行了仿真,并对4种不同的控制方法进行了峰时节点电压、线路负荷和日负荷曲线的比较和分析。
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